Enterprise AI Gateway Solutions
A provider-neutral control layer for teams standardizing model access, AI policy, routing, observability, and spend governance across production applications.
Why a solutions hub
Bring AI infrastructure decisions into one governed layer
Enterprise AI teams rarely have one model, one vendor, or one risk profile. Ferro Labs AI Gateway gives platform, security, and product teams a shared runtime contract for every model request.
Model access
Expose a stable API path for teams while provider credentials and model choices stay governed centrally.
Traffic control
Route by priority, latency, cost, condition, or experiment without forcing product code changes.
Runtime safety
Apply rate limits, budget checks, response controls, and fallback rules before traffic reaches providers.
Operational review
Track request volume, provider health, latency, cost signals, and configuration changes from one layer.
Solution paths
Choose the enterprise AI workflow you need to control first
Each solution maps a buyer problem to the gateway capabilities that make rollout reviewable, repeatable, and independent of any single model provider.
Provider-neutral by design
Keep model choice flexible while the operating model stays consistent
Ferro Labs AI Gateway does not force a provider bet. It gives platform owners one place to approve models, change routing, enforce policy, and observe production AI traffic while application teams keep shipping.
Standardize model access without standardizing on one provider
Give every application team a consistent gateway path while platform teams retain control over approved providers, model aliases, credentials, and routing policy.
Put policy before requests leave your network
Centralize budgets, guardrails, logging, fallback behavior, and approval workflows at the gateway layer instead of rebuilding them in every AI feature.
Give security and procurement a concrete review surface
Document how AI traffic is routed, observed, rate limited, and changed over time so enterprise reviewers can assess the operating model before rollout.
Proof points
Built for the controls enterprise AI programs ask for first
2,505
catalog models
Teams can evaluate and route across the live Ferro Labs model catalog without hard-coding provider-specific paths.
8
routing strategies
Fallback, load balance, least latency, cost optimized, conditional, content-based, A/B test, and explicit single target routing.
6
built-in plugins
Guardrail, cache, logging, token, rate limit, and budget controls are available at the gateway layer.
Production changes without application rewrites
Update provider priorities, fallback chains, budgets, and model aliases in the gateway instead of coordinating code changes across every product surface.
Controls security teams can inspect
Keep traffic policy, secret handling, logs, and usage limits centralized so review teams can reason about AI infrastructure as a managed platform capability.
Map the gateway to your enterprise AI rollout
Bring us your current AI stack, provider mix, security constraints, and rollout targets. We will help translate them into a gateway architecture and operating plan.